Automatic cardiac therapy advisor with hidden markov model processing

ABSTRACT

A method of automatically determining which type of treatment is most appropriate for (or the physiological state of) a patient. The method comprises transforming one or more time domain measurements from the patient into frequency domain data representative of the frequency content of the time domain measurements; processing the frequency domain data to form a plurality of spectral bands, the content of a spectral band representing the frequency content of the measurements within a frequency band; forming a weighted sum of the content of the spectral bands, with different weighting coefficients applied to at least some of the spectral bands; determining the type of treatment (or physiological state) based on the weighted sum.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of and claims priority toU.S. application Ser. No. 11/136,327, filed on May 24, 2005, now U.S.Pat. No. 8,165,671.

TECHNICAL FIELD

This invention relates to techniques for automatically advising as tothe appropriate cardiac therapy for a patient, e.g., the particulartherapy to be used for cardiac resuscitation.

BACKGROUND

The heart relies on an organized sequence of electrical impulses inorder to beat effectively. Any deviation from this normal sequence isknown as “arrhythmia.” A class of devices includes signal processingsoftware that analyzes electrocardiography (ECG) signals acquired fromthe victim to determine when a cardiac arrhythmia such as ventricularfibrillation (VF) or shockable ventricular tachycardia (VT) exists.These devices include automated external defibrillators (AEDs), ECGrhythm classifiers, or ventricular arrhythmia detectors. An AED is adevice that literally “talks” the provider through a process ofevaluating a patient for, attaching the patient to, and activating, theAED therapy. This device is capable of recognizing the two distinctcardiac waveforms: VT and VF.

VT is a tachydysrhythmia originating from a ventricular ectopic focus,characterized by a rate typically greater than 120 beats per minute andwide QRS complexes. VT may be monomorphic (typically regular rhythmoriginating from a single focus with identical QRS complexes) orpolymorphic (unstable, may be irregular rhythm, with varying QRScomplexes). An example rhythm for an unstable VT is illustrated in FIG.1A. Depending on the rate and the length of time that the VT has beensustained, a heart in the VT state may or may not produce a pulse (i.e.,pulsatile movement of blood through the circulatory system). The cardiacactivity still has some sense of organization (note that the “loops” areall basically the same size and shape). If there is no pulse associatedwith this VT rhythm, then the VT is considered to be unstable and a lifethreatening condition. An unstable VT can be treated with an electricalshock or defibrillation.

Supraventricular tachycardia (SVT) is a rapid heartbeat that beginsabove the hearts lower chambers (the ventricles). SVT is an abnormallyfast heart rhythm that begins in one of the upper chambers of the heart(atria), a component of the heart's electrical conduction system calledthe atrioventricular (AV) node, or both. Although SVT is rarelylife-threatening, the symptoms which include a feeling of a racingheart, fluttering or pounding in the chest or extra heartbeats(palpitations), or dizziness can be uncomfortable.

VF is usually an immediate life threat. VF is a pulseless arrhythmiawith irregular and chaotic electrical activity and ventricularcontraction in which the heart immediately loses its ability to functionas a pump. VF is the primary cause of sudden cardiac death (SCD). Anexample rhythm for VF is illustrated in FIG. 1B. This waveform does nothave a pulse associated with it. There is no organization to this rhythm(note the irregular size and shape of the loops.) The pumping part ofthe heart is quivering like a bag of worms, and it is highly unlikelythat this activity will move any blood. The corrective action for thisrhythm is to defibrillate the heart using an electrical charge.

A normal heart beat wave starts at the sinoatrial node (SA node) andprogresses toward the far lower corner of the left ventricle.

A massive electrical shock to the heart can correct the VF and unstableVT rhythms. This massive electrical shock can force all the cardiaccells in the heart to depolarize at the same time. Subsequently, all ofthe cardiac cells go into a short resting period. The hope is that thesinoatrial node (SA node) will recover from this shock before any of theother cells, and that the resulting rhythm will be a pulse producingrhythm if not normal sinus rhythm.

For AEDs, algorithms to recognize the two waveforms VT and VF aredesigned to perform ECG analyses at specific times during a rescue eventof a patient using defibrillation and cardio-pulmonary resuscitation(CPR). The first ECG analysis is usually initiated within a few secondsfollowing attachment of the defibrillation electrodes to the patient.Subsequent ECG analyses may or may not be initiated based upon theresults of the first analysis. Typically, if the first analysis detectsa shockable rhythm, the rescuer is advised to deliver a defibrillationshock. Following the shock delivery, a second analysis is automaticallyinitiated to determine whether the defibrillation treatment wassuccessful or not (i.e., the shockable ECG rhythm has been converted toa normal or other non-shockable rhythm). If this second analysis detectsthe continuing presence of a shockable arrhythmia, the AED advises theuser to deliver a second defibrillation treatment. A third ECG analysismay then be initiated to determine whether the second shock was or wasnot effective. If a shockable rhythm persists, the rescuer is thenadvised to deliver a third defibrillation treatment.

Following the third defibrillator shock or when any of the analysesdescribed above detects a non-shockable rhythm, treatment protocolsrecommended by the American Heart Association and European ResuscitationCouncil require the rescuer to check the patient's pulse or to evaluatethe patient for signs of circulation. If no pulse or signs ofcirculation are present, the rescuer is trained to perform CPR on thevictim for a period of one or more minutes. The CPR includes rescuebreathing and chest compressions. Following this period of CPR, the AEDreinitiates a series of up to three additional ECG analyses interspersedwith appropriate defibrillation treatments as described above. Thesequence of three ECG analyses/defibrillation shocks followed by 1-3minutes of CPR, continues in a repetitive fashion for as long as theAED's power is turned on and the patient is connected to the AED device.Typically, the AED provides audio prompts to inform the rescuer whenanalyses are about to begin, what the analysis results were, and when tostart and stop the delivery of CPR.

One limitation associated with many AEDs is that current automated ECGrhythm analysis methods cannot function with extra noise due to CPRchest compressions. Thus, conventional practice is to interrupt chestcompressions while performing ECG rhythm analysis. Long interruptions ofchest compressions have been shown to result in higher failure rate ofresuscitation. Many studies have reported that the discontinuation ofprecordial compression can significantly reduce the recovery rate ofspontaneous circulation and 24-hour survival rate. These studies include“Adverse effects of interrupting precordial compression duringcardiopulmonary resuscitation” by Sato et al. (Critical Care Medicine,Volume 25(5), May 1997, pp 733-736), “Adverse Outcomes of InterruptedPrecordial Compression During Automated Defibrillation” by Yu et al.(Circulation, 2002), and “Predicting Outcome of Defibrillation bySpectral Characterization and Nonparametric Classification ofVentricular Fibrillation in Patients With Out-of-Hospital CardiacArrest” by Eftestøl et al. (Circulation, 2002). Thus, it is useful torecognize abnormal heart rhythms during chest compressions.

There is recent clinical evidence showing that performing chestcompressions prior to defibrillation under some circumstances can bebeneficial. Specifically, it is clinically beneficial to treat a patientwith chest compressions prior to defibrillation if the response times ofthe medical emergency system result in a delay of more than four minutessuch that the patient is in cardiac arrest for more than four minutes.If the response times of the medical emergency system result in acapability to treat the patient in sooner than a four minute delay, itcan be better for the patient to be treated with defibrillation first.Methods have been developed to determine from the ECG waveform bothwhether the patient has been in cardiac arrest for longer than the 4minutes as well as time independent measures of when the most optimaltime is to shock. “Non-invasive monitoring and treatment of subjects incardiac arrest using ECG parameters predictive of outcome” by Brown andDzwonczyk (U.S. Pat. No. 5,683,424) describes methods to determine fromthe ECG waveform whether the patient has been in cardiac arrest forlonger than the 4 minutes. “Method and system for predicting theimmediate success of a defibrillatory shock during cardiac arrest” (U.S.Pat. No. 6,171,257 by Weil et al.) and “Ventricular Fibrillation ScalingExponent Can Guide Timing of Defibrillation and Other Therapies” byMenegazzi et al. (2004 American Heart Association, Inc.) describe timeindependent measures of when the most optimal time is to shock. Thesealgorithms use spectral analysis of the ECG to predict defibrillationshock success in some manner. Current methods utilizing spectralanalysis of the ECG for chest compression artifact rejection,defibrillation success prediction, and therapeutic decision-makingtypically specify a set of parameters in the ECG frequency spectrum tobe detected. For example, U.S. Pat. No. 5,683,424 compares a centroid ora median frequency or a peak power frequency from a calculated frequencyspectrum of the ECG to thresholds to determine if a defibrillating shockis necessary. These parameters do not uniquely specify the frequency ortime domain characteristics. For example, the median frequency of theECG spectrum for almost all patients in ventricular fibrillationdecreases initially then increases again after several minutes, makingit difficult to use median frequency to predict how long a patient hasbeen in cardiac arrest. Thus, the patient can have the same medianfrequency at widely differing durations of cardiac arrest. Usingamplitudes of the frequency spectrum of the ECG can be limited becausethe amplitudes are dependent on both the cardiac electrical output aswell as position of the ECG lead electrodes on the patient.

Some conventional automated ECG rhythm analysis methods detect VF andother arrhythmic heart rhythms by using spectral analysis of the ECGsignals with the assumption that the difference in the power spectrumbetween ECGs of normal heart rhythms and abnormal rhythms is such thatduring the abnormal rhythm the ECG is concentrated or mainly sinusoidalin a narrow band of frequencies between 4 and 7 Hz, while in normalrhythm the ECG is a broadband signal with major harmonics up to at least25 Hz. For example, “Comparison of four techniques for recognition ofventricular fibrillation from the surface” by Clayton et al. (ECG.Medical & Biological Engineering & Computing 1993; 31:111-117) and“Algorithmic sequential decision-making in the frequency domain for lifethreatening ventricular arrhythmias and imitative artifacts: adiagnostic system” by Barro et al. (Journal of Biomedical Engineering,1989, Volume 11) analyze the frequency domain of the ECG to check if theECG is mainly sinusoidal in the narrow band of frequencies. One problemwith these conventional methods is that CPR changes the assumptionbehind the methods so that VF and other dangerous rhythms cannot betypically detected during chest compressions.

Adaptive filters have been used in many studies to remove the artifactdue to CPR chest compression from the ECG signal. These studies include“CPR Artifact Removal from Human ECG Using Optimal MultichannelFiltering” by Aase et al. (IEEE Transactions on Biomedical Engineering,Vol. 47, No. 11, November 2000), “Removal of CardiopulmonaryResuscitation Artifacts From Human ECG Using an Efficient MatchingPursuit-Like Algorithm” by Husoy et al. (IEEE Transactions on BiomedicalEngineering, Vol. 49, No. 11, November 2002), “and U.S. Pat. No.6,390,996 by Halperin et at (2002). The adaptive filters use compressiondepth and thoracic impedance as reference signals to estimate theartifacts in the ECG signal. The adaptive filter's parameters areupdated by calculating the inverse of a cross-correlation matrix or theauto- and cross-spectra of the signal. The artifacts could be reducedwhen these adaptive filters were applied. However, there is usually asignificant part of the artifact left in the estimated ECG signal.Moreover, the adaptive-filter algorithm sometimes has a highcomputational complexity.

These adaptive filtering methods use the compression depth as thereference signal to remove the chest compression artifact from the ECGsignals. This is based on the assumption that the chest compressionartifact is correlated with the reference signal (compression depth) andindependent of the desired ECG signal. This can be true for aninfinitely long ECG signal but the estimated coefficients can be biasedif a limited length of the ECG signal is applied. It is also possiblethat the reference signals (such as the compression depth) can provideonly part of the information about the CPR artifact presented in the ECGsignal, i.e. the noise-reduction ability of the adaptive filter islimited by its knowledge of the noise. Fitzgibbon et al. in“Determination of the noise source in the electrocardiogram duringcardiopulmonary resuscitation” (Critical Care Medicine 2002 Vol. 30, No.4) reported that the thoracic impedance variation due to ventilation orchest compression has little correlation with the artifact in ECGrecording during chest compressions. Fitzgibbon et al. (2002) furthersuggested that the source of the noise in the signal during chestcompressions is the electrode motion and related to the electrode'selectrical properties, which makes the relation between the noise andthe compression depth more complicated. Thus, the artifact cannot besufficiently attenuated for satisfactory results with the conventionaladvisory algorithm for fibrillation detection.

One method for evaluating medical tests is to determine a test's abilityto correctly detect disease, also known as sensitivity, and the test'sability to avoid labeling normal things as disease, also known asspecificity. Ideally, a medical test has 100% sensitivity and 100%specificity. When a medical test is imperfect, sensitivity andspecificity are plotted on a graph called a receiver-operatorcharacteristics (ROC) curve. Variables in the medical test can be chosensuch that the resulting point of the medical test on the ROC curve isclosest to a point with 100% sensitivity and 100% specificity.

SUMMARY

In general, the invention features automatically determining which of aplurality of possible cardiac interventions should be performed intreatment of a patient. Prior information representative of priorcardiac interventions performed on the patient, and informationrepresentative of the patient's reactions to the prior cardiacinterventions, are stored, and the information is processed using ahidden Markov model to determine which of a plurality of possiblefurther cardiac interventions should be performed.

In preferred implementations, one or more of the following features maybe incorporated. The patient's reaction to the further cardiacintervention is sensed; further information representative of thefurther cardiac intervention, and of the patient's reaction to thefurther cardiac intervention, is stored; and the prior and furtherinformation is processed using a hidden Markov model to determine whichof still further cardiac interventions should be performed in furthertreatment of the patient. The patient is a cardiac arrest victim.

In other aspects, the invention features a method of automaticallydetermining which type of treatment is most appropriate for (or thephysiological state of) a patient. The method comprises transforming oneor more time domain measurements from the patient into frequency domaindata representative of the frequency content of the time domainmeasurements; processing the frequency domain data to form a pluralityof spectral bands, the content of a spectral band representing thefrequency content of the measurements within a frequency band; forming aweighted sum of the content of the spectral bands, with differentweighting coefficients applied to at least some of the spectral bands;determining the type of treatment (or physiological state) based on theweighted sum.

In preferred implementations, one or more of the following features maybe incorporated. The weighting coefficients may be ones chosen using aregression analysis comparing actual time domain measurements and actualoutcome of therapy for a population of patients. The weightingcoefficients may have been chosen to improve a correlation between theweighted sum and the outcome of therapy. The weighting coefficients aredifferent for at least two different therapy stages. The therapy may becardiac resuscitation, and the measurement comprises ECG signals. Themeasurement may comprise ECG signals, and the therapy stages maycomprise at least arrival at patient's side, pre-shock, and post-shock.The therapy stage may be based at least in part on rescuer entered dataindicative of the stage of therapy. The therapy stage may be based atleast in part on rescuer entered data indicative of at least what drugshave been delivered to the patient. The rescuer entered data may befurther indicative of whether the patient has been intubated, andwhether an automatic external chest compressor has been used. Thedetermining may comprise comparing the weighted sum to a threshold. Thethreshold may be different for at least two therapy stages. When thetherapy is cardiac resuscitation, and the measurement comprises ECGsignals, if the weighted sum exceeds the threshold the type of treatmentdetermined to be appropriate may be delivery of a defibrillation shock.The threshold used when the therapy stage is arrival at the patient'sside may be lower than the threshold used for later therapy stages.

In other aspects, the invention features a method of automaticallydetermining which type of treatment is most appropriate for a cardiacarrest victim, the method comprising transforming one or more timedomain electrocardiogram (ECG) signals into a frequency domainrepresentation comprising a plurality of discrete frequency bands,combining the discrete frequency bands into a plurality of analysisbands, wherein there are fewer analysis bands than discrete frequencybands, determining the content of the analysis bands, and determiningthe type of treatment based on the content of the analysis bands.

In preferred implementations, one or more of the following features maybe incorporated. Transforming may comprise the Fourier transform.Transforming may comprise a Wavelet transform. Transforming may comprisea Radon transform. Determining the content of the analysis bands maycomprise determining a plurality of values. The content and theplurality of values may be calculated at more than two points in time,and wherein the sequence of plurality of values in time may define atrajectory. The trajectory may be analyzed using estimation andprediction methods. The analysis method may involve use of a recursivefilter. The recursive filter may be a Kalman filter. The analysis methodmay involve use of a Particle Filter. The analysis of the trajectory maybe used to predict defibrillation success. The analysis of thetrajectory may be used to determine whether it is appropriate todefibrillate or deliver an alternative therapy such as chestcompressions, drugs such as epinephrine, constitutive nutrients such asglucose, or other electrical therapy such as pacing. A mathematicaltransformation may be performed on the trajectory. The transformationmay be a projection of the trajectory onto a plane within the parameterspace. Image mensuration algorithms may be employed to evaluate thefeatures of the two dimensional projection of the trajectory. Thecontent may comprise at least two parameters descriptive of the contentof an analysis band from the analysis bands. Determining the content ofan analysis band may comprise quantifying the energy within an analysisband. Quantifying the energy within an analysis band may comprisedetermining at least one number characterizing the energy of the highestpeak within the band. Quantifying the energy with an analysis band maycomprise determining an overall or average energy for the band. Theinvention further comprises analyzing the variation over time of thecontent of the analysis bands. The bands may be about 0.5 Hz in width.The bands may be of unequal widths such that additional resolution isprovided for frequency bands that are of greater importance in theanalysis. Frequencies less than 3 Hz may be subdivided into bands whosewidths are larger than those in the 6-12 Hz frequency range. Each bandmay be composed of an aggregation of multiple spectral measurements.Characteristics of the distribution of spectral measurements within theband may include at least one of the following descriptors: meanspectral energy, spectral energy variance, median spectral energy,maximum spectral energy, or minimum spectral energy.

In another aspect, the invention features a method of automaticallydetermining which type of treatment is most appropriate for a cardiacarrest victim, the method comprising transforming one or more timedomain ECG signals into a frequency domain generally containing aplurality of peaks, processing the frequency domain representation tocharacterize at least a plurality of the peaks, wherein characterizing apeak comprises determining a plurality of parameters characterizing thepeak, and determining the type of treatment based on the parameterscharacterizing at least some of the peaks.

In preferred implementations, one or more of the following features maybe incorporated. The invention may further comprise analyzing thevariation over time of at least some of the plurality of parameterscharacterizing at least some of the plurality of peaks. The content andthe plurality of values may be calculated at more than two points intime, and wherein the sequence of plurality of values in time may definea trajectory. The trajectory may be analyzed using estimation andprediction methods. The analysis method may involve use of a recursivefilter. The recursive filter may be a Kalman filter. The analysis methodmay involve use of a Particle filter. The analysis of the trajectory maybe used to predict defibrillation success. The analysis of thetrajectory may be used to determine whether it is appropriate todefibrillate or deliver an alternative therapy such as chestcompressions, drugs such as epinephrine, constitutive nutrients such asglucose, or other electrical therapy such as pacing. A mathematicaltransformation may be performed on the trajectory. The transformationmay be a projection of the trajectory onto a plane within the parameterspace. Image mensuration algorithms may be employed to evaluate thefeatures of the two dimensional projection of the trajectory. Analyzingthe variation over time may comprise determining variation in thefrequency of a peak. Determining a plurality of parameterscharacterizing the peak may comprise estimating a shape of the peak.Estimating a shape of the peak may comprise using a non-linear curvefitting routine. The plurality of the peaks may comprise a largestamplitude frequency peak and peaks having a fraction of an amplitude ofthe largest amplitude frequency peak. The parameters may comprise afrequency of a peak, an amplitude of the peak, and a width of the peak.The parameters may comprise a depth of the peak. The parameters maycomprise a variance of the peak. The parameters may comprise a firstmoment of the peak. The invention further comprises determining areference frequency from the frequency domain and determining a varianceof the energy of the frequency domain using the reference frequency. Theinvention may also further comprise determining that the victim is in asinus arrhythmic state if the variance of the energy of the frequencyrepresentation is below a threshold. The reference frequency is one of amean frequency, a median frequency, a center frequency, and a peakfrequency. Determining the type of treatment may comprise determiningthat the type of treatment is to defibrillate the victim's heart if thefollowing conditions are met: the victim is determined to be in thesinus arrhythmic state, a frequency of a largest amplitude frequencypeak is less than a first threshold, and the number of peaks is lessthan a second threshold. Determining the type of treatment may comprisedetermining that the type of treatment is chest compressions to thevictim if the following conditions are met: the victim is determined tobe in the sinus arrhythmic state and a frequency of a largest amplitudefrequency peak is greater than a first threshold and if the number ofpeaks is less than a second threshold. Determining the type of treatmentmay comprise determining that the type of treatment is monitoring thevictim or drug therapy if the following conditions are met: the victimis determined to be in the sinus arrhythmic state, and the number ofpeaks is greater than a threshold. Determining parameters may comprisemeasuring a change of one or more parameters of the peaks in a range ofthe frequency spectrum over multiple digital time samples. Each peak maybe considered to retain an identity over the multiple digital timesamples if its amplitude and frequency do not change more than athreshold from one time sample to a subsequent time sample. Determiningthe type of treatment may comprise comparing an oscillation cycle rateof the change to a cycle rate band and if the cycle rate is in the band,determining that the type of treatment is to defibrillate the victim'sheart. Determining the type of treatment may further comprisedetermining that a defibrillating shock to the victim's heart issuitable therapy when the oscillation is at or near a maximum. For twoor more peaks the change may be a relative decrease, and whereindetermining the type of treatment may comprise comparing the relativedecrease to a threshold, and if the relative decrease is above thethreshold, the type of treatment may be chest compressions and thendefibrillation. The one or more parameters may comprise amplitudes ofthe peaks, the threshold may be about fifteen percent, and the multipledigital time samples may cover at least a ten second interval. For twoor more peaks, the change may be a relative increase, the parameters maycomprise frequency of the peaks, amplitude of the peaks, or width of thepeaks, and wherein determining the type of treatment may comprisecomparing the relative increase to a threshold, and if the relativeincrease is above the threshold, the type of treatment may bedefibrillation. For two or more peaks, the change may be a decrease, andthe parameters may comprise variance of the frequency of the peaks, andwherein determining the type of treatment may comprise comparing thedecrease to a threshold, and if the decrease is below a threshold, thetype of treatment may be defibrillation. The range of the frequencyspectrum may be six to twelve Hertz. The invention may further comprisecommunicating the type of treatment to one of a drug infusion device, aportable chest compression device, and a ventilator. The invention mayalso further comprise displaying an indication of the type of treatment.Displaying an indication of the type of treatment may comprisedisplaying a value of an estimation of an accuracy of the type.

In another aspect, the invention features a method of automaticallydetermining which type of treatment is most appropriate for a cardiacarrest victim, the method comprising transforming one or more timedomain ECG signals into a frequency domain representation, processingthe frequency domain representation to characterize the content of thefrequency domain representation in a band from about 6 to about 12 Hz,and determining the type of treatment based on the content in the band.

In preferred implementations, one or more of the following features maybe incorporated. The invention may further comprise relying on aventricular fibrillation (VF) or a ventricular tachycardia (VT) advisoryalgorithm to determine whether the victim is in VF or VT, and whereindetermining the type of treatment may comprise determining when todeliver a shock. The content in the band may comprise a quantitativemeasure representative of approximately the total energy in the band.

In another aspect, the invention features a method of automaticallydetermining which type of treatment is appropriate for a cardiac arrestvictim, the method comprising measuring at least one physiologicalsignal, determining at least two parameters related to the at least onephysiological signal, the at least two parameters forming a parameterset, repeating the measurement and calculation at more than two pointsin time to create a sequence of parameter sets, wherein the sequence ofparameter sets defines a trajectory, and analyzing the trajectory usingestimation and prediction methods that comprise the use of a recursivefilter.

In preferred implementations, one or more of the following features maybe incorporated. The recursive filter may be a Kalman filter. Theanalysis method may involve use of a Particle filter. The analysis ofthe trajectory may be used to predict defibrillation success. Theanalysis of the trajectory may be used to determine whether it isappropriate to defibrillate or deliver an alternative therapy such aschest compressions, drugs such as epinephrine, constitutive nutrientssuch as glucose, or other electrical therapy such as pacing. Amathematical transformation may be performed on the trajectory. Thetransformation may be a projection of the trajectory onto a plane withinthe parameter space. Image mensuration algorithms may be employed toevaluate the features of the two dimensional projection of thetrajectory. The predicted next state of the parameter set may be used todetermine the appropriate treatment. The method may be carried out by adevice configured to determine an appropriate therapy for a rescuer toperform on the victim. The probability of defibrillation successassociated with a plurality of alternative treatments may be shown onthe display of the device. The probability of success with a pluralityof treatments may be shown on the display as range of numbers. Thedevice may be an AED that notifies the rescuer in the form of an audibleor visual alarm indicating that the paramedic should stop doingcompressions for a more accurate analysis of the ECG waveform. Thedevice may be an AED that notifies the rescuer in the form of an audibleor visual alarm indicating that the paramedic should alter the therapybeing delivered.

In another aspect, the invention features an AED capable ofautomatically determining which type of treatment is appropriate for acardiac arrest victim, the AED comprising electrical therapy padsconfigured to deliver electrical therapy to patients, ECG electrodessmaller in diameter than the electrical therapy pads are integrated intothe electrical therapy pads, the smaller ECG electrodes are configuredto provide at least one additional electrical vector that isapproximately orthogonal to the monitoring vector produced by the ECGsignal from the therapy electrodes.

In preferred implementations, one or more of the following features maybe incorporated. A vector sum of the at least one additional electricalvector and the monitoring vector may provide a trajectory over time thatcan be used by the AED in determining which type of treatment isappropriate.

These and other implementations may have one or more of the followingadvantages. The method uses frequency-domain analysis methods for ECGprocessing/advisory during chest compressions. This method allows ECGanalysis without interruption of chest compression and thussignificantly reduces the interruption time during chest compressions,leading to an increase in the success rate of resuscitation.

Some implementations allow for the more complete specification of theECG waveform spectrum for different cardiac states in a mathematicallytractable form that provides improved receiver-operator characteristics(ROC) of the detection algorithm, while reducing the performance burdenon the processor.

Other features and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1A is a magnitude versus time plot of a ventricular tachycardia(VT) rhythm.

FIG. 1B is a magnitude versus time plot of a ventricular fibrillation(VF) rhythm.

FIG. 2 is a diagram of one implementation including an automaticelectronic defibrillator (AED) and a multiple lead electrocardiograph(ECG) device.

FIG. 2A is a diagram of the AED of FIG. 2.

FIG. 3A is an example of a frequency spectrum plot with the energyconcentrated within a small frequency range.

FIG. 3B is an example of a frequency spectrum plot with the energydistributed over a relatively larger frequency range.

FIG. 4 is an example of an ECG spectrum as a function of time. Themagnitude (or energy) of the spectrum is encoded by the grayscale. Adarker color corresponds to a higher magnitude.

FIG. 5 is an EFV score of the signal in FIG. 4 as a function of time.

FIG. 6 is a flow chart of a process for detecting VF in a patient duringchest compressions.

FIGS. 7A and 7B are examples of an ECG spectrum at two points in time,in this case separated by 4 seconds.

FIG. 8 shows a logistic curve that relates a measured predictivevariable (x axis) into a approximate probability of therapeutic success(y axis).

DETAILED DESCRIPTION

There are a great many different implementations of the inventionpossible, too many to possibly describe herein. Some possibleimplementations that are presently preferred are described below. Itcannot be emphasized too strongly, however, that these are descriptionsof implementations of the invention, and not descriptions of theinvention, which is not limited to the detailed implementationsdescribed in this section but is described in broader terms in theclaims.

Referring to FIG. 2, a rescuer uses an AED 10 to automatically monitor avictim during cardiac resuscitation. The AED 10 includes a speaker 16, adisplay screen 18, an analog to digital converter 20, a processor 22,and a defibrillator pulse generator 24. The analog-to-digital converter20 is connected to a set of ECG leads attached to the victim. The ECGleads monitor the electrical rhythms of the victim's heart. Theconverter 20 sends the signals from the ECG leads to the processor 22.The processor 22 monitors the victim's heart for dangerous rhythms usingthe ECG signals while the victim is resuscitated using chestcompressions techniques. If the AED 10 detects a dangerous heart rhythm,the AED 10 generates an alarm signal. The alarm signal is noticeable tothe rescuer. The AED 10 can generate a defibrillating shock to thevictim when the rescuer issues a command to the AED 10. Thedefibrillating shock is intended to remedy the dangerous rhythm of thevictim's heart.

The AED 10 uses a rhythm advisory method for a) quantifying thefrequency-domain features of the ECG signals; b) differentiating normaland abnormal ECG rhythms, such as VF; c) detecting the onset of abnormalECG rhythms; and d) making decisions about the physiological states ofthe heart. This frequency-domain measure is reliable with or without thepresence of the chest compression artifact in the ECG signals. The AED10, after identifying the current physiological state of the heart, canmake a decision about appropriate therapeutic action for the rescuer tomake and communicates the action to the rescuer using the speaker 16 andthe display screen 18.

This rhythm advisory method can also be incorporated in an ECG rhythmclassifier or a ventricular arrhythmia detector.

The AED 10 may incorporate functionality for performing additionaltherapeutic actions such as chest compressions, ventilations, ordelivery of intravenous solution containing metabolic or constitutivenutrients. Based on the results of the analysis of the rhythm advisorymethod, the AED 10 may automatically deliver the appropriate therapy tothe patient. The AED 10 may also be configured in “advisory” modewherein the AED 10 will prompt the caregiver after the AED 10 has made adetermination of the best therapy, and acknowledgement by thecaregiver/device operator, in the form of a button press orvoice-detected acknowledgement, is required before therapy is deliveredto the patient.

The AED 10 then analyzes the ECG signals to predict defibrillationsuccess as well as to decide whether it is appropriate to defibrillateor to deliver an alternative therapy such as chest compressions, drugssuch as epinephrine, constitutive nutrients such as glucose, or otherelectrical therapy such as pacing.

In some examples, one or more therapeutic delivery devices 30automatically deliver the appropriate therapy to the patient. Thetherapeutic delivery devices 30 are physically separate from thedefibrillator AED 10 and control of the therapeutic delivery devices 30may be accomplished by a communications link 32. The communications link32 may take the form of a cable connecting the devices 10, 30, butpreferably the link 32 is via a wireless protocol such as Bluetooth or awireless network protocol such as Institute of Electrical andElectronics Engineers (IEEE) 802.11. Bluetooth is a telecommunicationsindustry specification that describes how mobile computing devices canbe interconnected using a short-range wireless connection. Thetherapeutic delivery device 30 can be a portable chest compressiondevice that is commercially available as the Autopulse™, provided byRevivant of Sunnyvale, Calif. In other examples, the therapeuticdelivery device 30 is a drug infusion device that is commerciallyavailable as the Power Infuser™, provided by Infusion Dynamics ofPlymouth Meeting, Pa., or the Colleague CX™, provided by BaxterHealthcare Corp., of Round Lake, Ill. The therapeutic delivery device 30can be a ventilator that is commercially available as the iVent™,provided by Versamed of Pearl River, N.Y. The therapeutic deliverydevice 30 can also include multiple therapies such as defibrillation,chest compression, ventilation and drug infusion.

In other examples, control and coordination for the overallresuscitation event and the delivery of the various therapies may beaccomplished by a device 34 or processing element external to the AED10, for instance the device 34 may download and process the ECG datafrom the AED 10; analyze the ECG signals, perform the determinationsbased on the analysis, and control the other therapeutic devices 30,including the AED 10.

In other examples, the AED 10 may perform all the processing of the ECG,including analyzing the ECG signals, and transmit to the control device34 only the final determination of the appropriate therapy, whereuponthe control device 34 would perform the control actions on the otherlinked devices 30. The control device 34 is commercially available asthe Autopulse™, provided by Revivant of Sunnyvale, Calif.

The chest compression artifact can be separated from the ECG signalcomponents in the frequency domain. This makes it possible for the AED10 to process the ECG signal without halting the processing during CPRchest compressions. The compression rate during CPR chest compressionsrecommended by American Heart Association (2000) is 100 per minute or1.7 Hz and the frequency range used for quantifying the frequency-domainfeatures of the ECG signals can be set to be higher than that(preferably but not limited to be 3 Hz and up) using a high passfrequency filter.

The rhythm advisory method quantifies the energy distribution of the ECGsignal in the frequency domain with a quantification method. Thequantification result can be used to differentiate normal and dangerousECG rhythms with or without the presence of the chest compressionartifact. In one method, the AED 10 breaks up the frequency domain ofthe ECG signal into analysis frequency bands. The AED 10 then analyzesthe different frequency bands for energy or variation over time todetermine an appropriate treatment for the victim. In the preferredembodiment, the bands are 0.5 Hz in width, though they may also bedivided into unequal widths such that additional resolution is providedfor frequency bands that are of greater importance in the analysis. Forinstance, frequencies less than 3 Hz may be subdivided into only threeequally spaced bands while the range from 3-5 Hz may have 0.5 Hz bands,and the range of 6-12 Hz may have 0.25 Hz bands. Each band may becomposed of an aggregation of multiple spectral measurements. For eachband, characteristics of the distribution of spectral measurementswithin the band mayinclude such descriptors, e.g., as mean spectralenergy, spectral energy variance, median spectral energy, maximumspectral energy, minimum spectral energy.

In one example of the analysis frequency bands, the AED 10 generates thefrequency bands based on peaks in the frequency spectrum. Thus, onefrequency band corresponds to the frequency spread of a given peak inthe frequency spectrum. There are common algorithms for identifyingpeaks in the frequency spectrum that include calculating slopes andenergy at different points of the frequency spectrum. For each of thesepeaks, the AED 10 uses a non-linear parameter estimation algorithm orcurve fitting algorithm to estimate the shape of the peak. From thisspectral shape, the AED 10 calculates parameters about the peak.

The quantification method differentiates various spectral patterns andshapes. The AED 10 makes a decision about the physiological state of theheart and suitable therapy based on the quantification results. Thequantification method of the rhythm advisory method is a combination ofmeasures from sub-methods. Some of these sub-methods differentiatevarious spectral shapes, including but not limited to: (1) the number ofpeaks in the target frequency range, (2) the relative strength/peakvalue of various spectral peaks, (3) the relative bandwidth of variousspectral peaks and (4) the variance of the energy distributed in aselected frequency range. One or more sub-methods can also measurechange in the spectral information over time.

These measures can be combined in a multi-dimension space to enhanceboth the sensitivity and specificity of the decision. One or moreinformation processing techniques can be used to quantify thecombination following the computation of these measures in order to makea decision based on the combination. The information processingtechniques can include but are not limited to simple combining rules ormath, neural networks, expert systems incorporating fuzzy or standardlogic, or other artificial intelligence techniques. The additionalmeasures can also include measurement of velocity or acceleration ofchest compression during chest compressions according to the techniquestaught by U.S. application Ser. No. 10/704,366, Method and Apparatus forEnhancement of Chest Compressions During Chest Compressions, filed onNov. 6, 2003.

The information processing techniques include simple combining rules ormath, neural networks, expert systems incorporating fuzzy or standardlogic, or other artificial intelligence techniques. These techniquesmake a decision based on the combination of measures about thephysiological state of the heart and suitable therapy. The differentmeasures are individual indications that have varying degrees ofuncertainty about the physiological state of the heart and suitabletherapy. In some examples, the information processing technique istrained automatically using software techniques known to those skilledin this art and a database of ECG rhythms that include outcome data.These examples include neural networks. In other examples, theinformation processing technique is generated manually based onobservations of ECG patterns and outcomes. These examples include simplecombining rules or math, and expert systems utilizing fuzzy or standardlogic. In the example of expert systems utilizing standard logic, aprogrammer manually generates logical rules without uncertainty, therules specifying preconditions such as “if measure A recommendsdefibrillation” and “if measure B recommends defibrillation”, and ifthese preconditions are met, the AED 10 automatically defibrillates thepatient. In the example of expert systems utilizing fuzzy logic, therules are more “fuzzy” and the states to be combined incorporate somedegree of uncertainty based on human language. For instance, the fuzzylogic rules can incorporate such input as “measure A detects a strongneed for defibrillation” versus “measure A detects a weak need fordefibrillation”. The fuzzy logic framework combines the differentmeasures and outputs results such as “strong need for defibrillation” or“weak need for defibrillation”.

The method of making the decision about the physiological state is tochoose from a group of possible states, each of which corresponds to apredetermined value range of the proposed measure. The possible statescan include but are not limited to normal sinus rhythm, VF, shockable(unstable) VT, stable VT, supraventricular rhythm, and pulselesselectrical activity.

One possible sub-method for the quantification method is the variance ofthe energy distributed in a selected frequency range, or variancesub-method. Two examples of energy-distribution patterns are shown inFIGS. 3A and 3B. The frequency spectrum plots of FIGS. 3A and 3B arecalculated using a fast Fourier transform (FFT) of a signal over time.Referring to FIG. 3A, the energy Y₁(f) of a frequency spectrum 50 isconcentrated within a narrow frequency band and represents a patternfound in an arrhythmic state such as VF. Referring to FIG. 3B, theenergy Y₂(f) of a frequency spectrum 52 is distributed over a widefrequency range and represents a pattern found in a non-dangerous heartrhythm or normal sinus rhythm. The variance sub-method quantifies thefeatures of the two frequency spectra 50, 52 and thus the variancesub-method can differentiate between an arrhythmic state and normalsinus rhythm.

One example of the variance sub-method calculates the variance of theenergy from a reference frequency (F_(ref)) of the spectrum. Possiblecandidates of the reference frequency include but are not limited to themean frequency, the median frequency, the center frequency, or the peakfrequency of the spectrum.

In this example, the variance sub-method computes the weighted distanceof each frequency component from the reference frequency of the spectrumand thus quantifies the energy-distribution pattern. An example of thismeasure, the energy-frequency variance (EFV) can be calculated with thefollowing mathematical equation:

${E\; F\; V} = \frac{\int{\left( {f - F_{ref}} \right)^{2} \times {Y(f)}{\mathbb{d}f}}}{\int{{Y(f)}{\mathbb{d}f}}}$

However, the variance sub-method is not limited to this mathematicalequation. Measures that quantify the weighted or un-weighted distance ofthe frequency components from a reference frequency of the frequencyspectrum can be used for this measure.

Referring to FIG. 3A, energy of the spectrum 50 is concentrated within anarrow frequency range and thus the spectrum has a relatively small EFVvalue. Referring to FIG. 3B, energy of the spectrum 52 is distributedover a relatively wider frequency range and the spectrum has arelatively larger EFV value. Thus, the EFV value can be used todistinguish between a normal sinus rhythm and an arrhythmic sinus rhythm(e.g., VF).

Referring to FIG. 4, a spectrum 100 of a piece of an ECG signal is afunction of time. Part 102 of the signal shows a VF rhythm during chestcompressions. Part 104 of the signal shows a VF rhythm without chestcompressions. The VF is terminated by an electrical shock 106, which isfollowed by a period of normal sinus rhythm (NSR). During this NSRperiod, part 108 has no chest compressions while part 110 has chestcompressions. Chest compression artifacts that are characterized bystrong low-frequency (below 3 Hz) components can be observed in thefirst 15 seconds (part 102) and the last 10 seconds (part 110) of thistime-frequency plot 100. During the time periods 102 and 104 that areassociated with VF (i.e. before the electrical shock 106), the energydistribution Y(f) above 4 Hz is clearly concentrated in a smallfrequency range, with or without the presence of the chest compressionartifact. During the time periods 108 and 110 of NSR (i.e. after theelectrical shock 106), the energy distribution Y(f) above 4 Hz has apattern that the energy is distributed over a wide frequency range, withor without the presence of the chest compression artifact.

Referring to FIG. 5, an EFV score 152 is calculated from the signal 100(shown in FIG. 4). A threshold 154 can be used to distinguish anarrhythmic rhythm from a normal sinus rhythm. Thus, during the first 50seconds (parts 102 and 104 having VF rhythm) of the signal 100, the EFVscore 152 is below the threshold 154.

Referring to FIG. 6, a variance sub-method 200 is implemented in thesoftware and/or hardware of the AED 10. The ECG data acquired by thefront-end analog to digital converter 10 of the AED 10 is processed in asegment-by-segment manner. The number of segments to be processed beforea decision is made is predetermined (e.g., 9 segments).

The length of a segment is preferably 2 seconds and each segmentpreferably has a 1-second overlap with both the segment before and afteritself, for the desired frequency and time-domain resolution.

The segment-counter is set (202) to be zero when the processing startsand the first segment of the signal is acquired (204). A high-passfilter with a desired cutoff frequency (preferably but not limited to be0.5 Hz) is then applied (206) to remove the baseline drift. Thefrequency-domain representation of the filtered signal is acquired via afast fourier transform (FFT) (208). The spectral shape is quantified(210) using a preferred method. In an example, the EFV score iscalculated based on this frequency-domain representation and thefrequency range for the EFV calculation is selected such that thelow-frequency part where the chest compression artifact dominates isexcluded.

The segment counter is increased (212) by one after the quantificationof the spectral shape. If (214) all of the predetermined number ofsegments have been processed, the quantification results are processed(216) to get a final score (including but not limited to the mean valueof the EFV scores), otherwise the next segment of ECG signal isprocessed. In some implementations, the final score is an average of thescores from the segments.

An estimate of the physiological state of the heart can be made based onthe final EFV score. If (218) the final score is below a predeterminedthreshold, an arrhythmic rhythm is estimated (220). Using the variancesub-method, the AED 10 compares a threshold to the final EFV score todetermine if the victim is in an arrhythmic state. Otherwise theprocessed signal is estimated to be normal. In one example, a presetthreshold of 6 is used. In other examples, other preset thresholds canbe used.

An arrhythmic sinus rhythm can be detected using the variancesub-method. These arrhythmic sinus rhythms can be different types ofrhythms with different appropriate therapies. It may be difficult todistinguish between arrhythmic rhythms that are shockable rhythms andunshockable rhythms using only the variance sub-method. For example, VTsthat are shockable (rates exceeding 120-150 beats per minute [BPM]) maynot be distinguishable from non-shockable VTs (<120 BPM) solely with themeasure from the variance sub-method. Thus, the quantification methodpreferably enhances the variance sub-method with at least one otherspectral measurement in determining the appropriate therapy for detectedsinus rhythms. The quantification method may also make decisions basedon changes in the spectral parameters over time. Multiple measures maybe thought of as forming a matrix, but actual implementations need notemploy matrices.

In some implementations, the AED 10 may combine the frequency of thelargest amplitude spectral peak (LASP) in the frequency spectrum withthe measure from the variance sub-method to create a 1×2 matrix. In someimplementations, AED 10 may additionally calculate the number ofspectral peaks in the frequency representation of the ECG signal withamplitudes of at least 25% of the LASP using conventional methods knownto those skilled in the art of signal processing and spectral analysisand include this measurement in the vector. A frequency of the LASP(FLASP) of less than 2 Hz and the number of peaks (NOP) less than 3indicates that it is a shockable VT or VF, while a FLASP of greater than2 Hz and an NOP of less than 3 indicates a non-shockable VT.Non-shockable supraventricular rhythms can have a NOP greater than 3.

In other implementations, the AED 10 can combine information from thevariance sub-method and the FLASP and NOP measure, using informationprocessing techniques described previously, to estimate thephysiological state of the heart and suitable therapy. A combination ofthe EFV under a threshold and FLASP<2 Hz and NOP<3 can indicate ashockable VT or VF for which appropriate therapy can be defibrillation.A combination of the EFV under a threshold and FLASP>2 Hz and NOP<3 canindicate a non-shockable VT for which appropriate therapy can be normalCPR. A combination of the EFV under a threshold and NOP>3 can indicate asupraventricular rhythm for which appropriate therapy can be simplymonitoring the patient or drug therapy.

A descriptor matrix may take the form of a [n×m] dimensional matrix,where n=the number of peaks and m=the number of parameters used todescribe the spectral shape. In one implementation with m=6, the sixparameters are the following: 1) the frequency of the particular peak(FP); 2) the amplitude of that peak (AP); 3) the width of the peak (PW);4) the depth of the peak (DP); 5) the variance of that peak (VP); and 6)the first moment of that peak (FM). Peak number (PN) is a digitproviding an identifier for each individual peak. For instance,initially the AED 10 detects 5 peaks, each PN numbered sequentially withfrequencies at 1, 2, 3, 4, and 5 Hz. Four seconds later in time,however, the AED 10 detects a peak at a new frequency of 4.5 Hz and thepeak is assigned a PN of 6.

The description matrix, which may be termed a spectral shape matrix(SSM), may include two header values, NOP and a boolean value, Gaussianpeak (GP), which indicates that for spectral shapes that have a singlepeak (NOP=1) and GP=true, that the spectral shape may be described by aparameter subset of only FP, AP, and VP. The SSM may preferably take theform:

$\begin{matrix}{FP}_{1} & {AP}_{1} & {PW}_{1} & {VP}_{1} & {F\; M_{1}} \\{FP}_{2} & {AP}_{2} & {PW}_{2} & {VP}_{2} & {F\; M_{2}} \\{FP}_{3} & {AP}_{3} & {PW}_{3} & {VP}_{3} & {F\; M_{3}} \\\ldots & \ldots & \ldots & \ldots & \ldots \\\ldots & \ldots & \ldots & \ldots & \ldots \\{FP}_{n} & {AP}_{n} & {PW}_{n} & {VP}_{n} & {F\; M_{n}}\end{matrix}$

Since fibrillation is a chaotic rhythm, the FP frequencies may vary at arate faster than the time window of the short-time Fourier transform.For instance, if the time window for computing the Fourier transform togenerate a frequency spectrum is set for 4 seconds, the FPs for adjacenttime windows (and corresponding frequency spectrums) will appear to jumpfrom one frequency to the next. If, however, the AED 10 applies astandard short time Fourier transform to the signal while at the sametime increasing the rate at which a Fourier transform is performed onthe incoming data, the time window will be reduced and thus there willbe a loss in the spectral resolution of the Fourier transform. Thus, inone example, the AED 10 simultaneously performs multiple Fouriertransforms on the ECG data with each subsequent transform initiated 400milliseconds after initiation of the previous transform and a timewindow of 4 seconds, resulting in the AED performing 10 simultaneoustransforms of data in a time window of 4 seconds. Thus, the data foreach transform has some overlap with data for adjacent transforms. Insuch a manner, the AED 10 maintains both spectral and time resolution.

The AED 10 may calculate additional header values that describe genericaspects of the ECG spectrum. These additional header values may include,for instance, the amplitude spectrum area (AMSA) as described in U.S.Pat. No. 5,957,856 or the variance measure, as described previously.These values, along with NOP and GP, can be thought of as forming avector on which matrix operations and transformations may be performedindependently of, or combined with, the matrix formed by the parametersfor the individual peaks.

The AED 10 can then perform matrix operations and transformations knownto those skilled in the art on the SSM. The AED 10 can also calculatethe SSM at regular intervals in time, to generate a [n×m×p] dimensionalmatrix, where p is the number of samples in the time interval ofinterest. Each SSM may be thought of as a point in [n, m]-space thatthen forms a trajectory in the [n, m, p]-space. The AED 10 then analyzesthis trajectory to predict defibrillation success as well as to decidewhether it is appropriate to defibrillate or deliver an alternativetherapy such as chest compressions, drugs such as epinephrine,constitutive nutrients such as glucose, or other electrical therapy suchas pacing.

The AED 10 may identify one or more peaks in the frequency spectrum. Foreach of these identified peaks, the AED 10 identifies a frequency bandcorresponding to the peak. The AED 10 may determine the peak modelparameters, e.g. FP, AP, and PW, iteratively by a nonlinear parameterestimation or curve fitting routine for each peak's frequency band. Forexample, the AED 10 may use the Marquardt-Levenberg algorithm tominimize the error in the nonlinear parameter estimation or Chi-square,χ², where χ² is expressed as follows.

${\chi^{2}(p)} = {\frac{1}{N - P}{\sum\limits_{i}{\left\lbrack \frac{{M(i)} - {S\left( {i;p} \right)}}{\sqrt{M(i)}} \right\rbrack^{2}.}}}$For this expression, there are N recorded energy values, M(i) are therecorded energy values, and S(i; p) is the synthesized model curveenergy values, sampled at points i in dependence on p varying parametervalues. The term enclosed in brackets corresponds to the normalizedresiduals R(i), which provide a weighted measure of the differencebetween the fit curve and the data at each measured frequency valueM(i).

The AED 10 uses either the height-normalized Lorentzian function, L(E),or the Gaussian function, G(E) to model an energy function for each ofthe spectral peaks where E is a frequency. In the case of L(E):

${L(E)} = {\left\{ {1 + \left\lbrack \frac{E - E_{0}}{\beta} \right\rbrack^{2}} \right\}^{- 1}.}$In the case of G(E):

${G(E)} = {\exp\left\{ {{- \ln}\mspace{11mu}{2 \cdot \left\lbrack \frac{E - E_{0}}{\beta} \right\rbrack^{2}}} \right\}}$Both functions L(E), G(E) are completely characterized by the peakparameters β, corresponding to ½ the peak width at half-maximum peakamplitude and E₀, the peak position or FP. The AED 10 can model skew ofthe peak by combining the Gaussian G(E) and Lorentzian L(E), with βreplaced by the term β+α(E−E₀). The AED 10 can also add in a factor h toallow for varying peak heights. The result is function ƒ(E). The AED 10calculates ƒ(E) as follows.

${f(E)} = {{h \cdot \left\{ {1 + {M \cdot \left\lbrack \frac{E - E_{0}}{\beta + {\alpha\left( {E - E_{0}} \right)}} \right\rbrack^{2}}} \right\}^{- 1} \cdot \exp}\left\{ {{\left( {{- 1} - M} \right) \cdot \ln}\mspace{11mu}{2 \cdot \left\lbrack \frac{E - E_{0}}{\beta + {\alpha\left( {E - E_{0}} \right)}} \right\rbrack^{2}}} \right\}}$

Some of the advantages of this product-type peak shape model ƒ(E) arethe availability of analytical presentations of the partial derivativesof ƒ(E) with respect to the parameters, which are needed in theMarquardt-Levenberg algorithm to establish the Jacobi matrix, theanalytical value of β, and a faster convergence of the iterativeestimation process. The depth of each peak is estimated either byincorporating a baseline curve into the Marquardt-Levenberg algorithm,or by simply determining the two minimum points of the spectrum for aregion around the estimated peak. Thus, using techniques known to oneskilled in this art, the AED 10 can compute the spectral shapeparameters of the peak: FP, AP, PW, DP, VP, and FM from the functionθ(E).

If the AED 10 finds a peak in the immediately subsequent time intervalfor which the AP and FP value does not vary by more than preferably 10%,then that second peak is considered to have the same peak number, PN,indicating that it is the same peak with a shift in frequency andamplitude. In such a fashion, the AED 10 can develop trajectories forthe parameters for each particular peak as well as for the overalldescriptor matrix. The AED 10 can add a new peak at any time during theevent, in which case the AED 10 gives the new peak a new PN value. Ifthe AED 10 determines that a peak is extinguished, the PN number ismaintained in memory of the AED 10. In the processing of candidates fornew peaks, the sub-method reviews all extinguished peaks to firstdetermine if the new peak is actually an extinguished peak, in whichcase the candidate is not given a new PN, and instead is given the PNnumber of the extinguished peak.

Prior to a successful shock of a heart in a dangerous rhythm, one ormore parameters AP, DP, VP, FP, PW of peaks in the 6-12 Hz range of thefrequency spectrum can oscillate with a cycle rate in the range of 0.1-1Hz. Thus, detection of this oscillation through multiple time windowsand frequency spectrums can be incorporated into the informationprocessing technique as an additional sub-method that can recommenddefibrillating the heart. Furthermore, the sub-method can recommendtiming the defibrillating shock when the peaks are at a maximum energyin the 0.1-1 Hz cycle. For example, the sub-method can recommend timingthe delivery of the defibrillation shock to occur during the 100millisecond Fourier transform cycle when the APs in the 6-12 Hz regionare at a maximum. When the particular AP-maximum cycle has be found, theAED 10 waits to deliver the defibrillation shock until the AED 10detects the peak of the waveform after it has been band pass-filteredwith a center frequency of 7 Hz. This sub-method synchronizes the shockwith the elements of the ECG waveform that are most related to thenormal sinus QRS.

The parameters FP, AP, and PW of peaks in the 6-12 Hz region may alsoundergo oscillations indicating a change in the state of the heart asshown in FIGS. 7A and 7B, which depict the spectrum as measured at twopoints in time, separated by an interval of 4 seconds. For a heart thathas been in fibrillation for a period of time, the ECG undergoes agradual degradation in the values of the parameters FP, AP, and PW ofpeaks in the 6-12 Hz region of the frequency spectrum. As describedpreviously, suitable therapy for a heart that has been in fibrillationfor a period time is to do chest compressions and then defibrillate.This degradation is measured over at least a 8-10 second interval. Thisis an additional sub-method for the information processing technique.For example, if the AED 10 detects the APs of at least two peaks in the6-12 Hz region of the frequency spectrum decreasing by at least 15% overa 10 second interval, the sub-method recommends chest compressions andthen defibrillation.

If the circulation and metabolic substrate of the heart improve to thepoint that the heart is more likely to be able to recover from adefibrillation shock, changes in the parameters FP, AP, and PW of peaksin the 6-12 Hz region of the frequency spectrum will provide precursorsto changes in the ECG that might be seen in the time domain of the ECGsignal, such as an increase in the amplitude of the ventricularfibrillation ECG (often termed “coarsening” by medical practitioners).If the AED 10 detects an increase in the parameters FP, AP, DP, VP or PWof peaks in the 6-12 Hz region of the frequency spectrum, for instanceas shown in FIG. 7B, a sub-method will recommend ceasing chestcompressions or other current therapy and then defibrillation.

The peak frequencies, FP, for the peaks in the 6-12 Hz region of thefrequency spectrum can vary over time less when the condition of theheart is improving and thus the heart can handle the shock ofdefibrillation. This may be due to the presence in the myocardialactivations of more normal activity at low levels manifesting inharmonics of the sinus rhythm fundamental frequency. This variation inthe peak frequencies may be measured as the ratio of the average changein frequency in the region of 6-12 Hz with that of the FPs in thefrequency range of 3-6 Hz or measured as an absolute change for FPs inthe range of 6-12 Hz. This sub-method, upon detecting the variation inthe peak frequencies, recommends defibrillation to the informationprocessing technique.

It is also possible for a sub-method to project the [n×m×p] trajectoryof the SSM matrix onto a plane within the [n×m]-space and then analyzethe form taken by the projection of the trajectory in the plane todetermine the appropriate time to shock or the optimal treatment. Theprojection may include up to (n+m) variables of different weightings,though it preferably is a projection that is primarily along the VP axisof the [n×m]-space. In the plane projection, image mensurationalgorithms are employed to evaluate the features of the two dimensionalprojection of the trajectory. The following are some of the preferredmensuration classes for which measurements are made by means known tothose skilled in the art: area, centroid, circularity, clustering,compactness, maximum axis, minimum axis, and perimeter. For instance,the minimum axis may be determined as follows. The minimum axis of anobject is formally defined as the axis of maximum inertia (dispersion)passing through the centroid. One method to calculate the minimum axisis to compute the eigenvalues and eigenvectors of the scatter matrixcomprised of the coordinate points of the object. The eigenvectorcorresponding to the smallest eigenvalue is the minimum axis. Anothermethod is to fit an ellipse to the object perimeter.

The projection may be calculated for a specific duration of time, forinstance 10 seconds, resulting in a series of 2-dimensional objects thatare representations of the trajectory in time-so-called projection“snap-shots”. It then becomes possible to analyze trends in the timeseries of values in the mensuration classes for changes indicative ofimproving physiological conditions. For instance, an increased amplitudein VP oscillation during VF is indicative of an improving physiologicalstate. In this case, the AED 10 would then provide feedback to thecaregiver to continue performing the rescue operation as they have withan audible prompt such as, “Keep up the good work. The patient'scondition is improving.” Other such mensuration classes that are ofvalue to track over time are the maximum axis angle, the perimeter andcompactness.

Methods such as the Kalman filter may be used for the estimation andprediction of the trajectory. The Kalman filter estimates a process byusing a form of feedback control: the filter estimates the process stateat some time and then obtains feedback in the form of (noisy)measurements. As such, the equations for the Kalman filter fall into twogroups: time update equations and measurement update equations. The timeupdate equations are responsible for projecting forward (in time) thecurrent state and error covariance estimates to obtain the a prioriestimates for the next time step. The measurement update equations areresponsible for the feedback—i.e. for incorporating a new measurementinto the a priori estimate to obtain an improved a posteriori estimate.The time update equations can also be thought of as predictor equations,while the measurement update equations can be thought of as correctorequations. Indeed the final estimation algorithm resembles that of apredictor-corrector algorithm for solving numerical problems.

Discrete Kalman filter time update equations:{circumflex over (x)} _(k) ⁻ =A{circumflex over (x)} _(k-1) +Bu _(k-1)P _(k) ⁻ =AP _(k-1) A ^(T) +Q

Discrete Kalman filter measurement update equations:K _(k) =P _(k) ⁻ H ^(T)(HP _(k) ⁻ H ^(T) +R)⁻¹x _(k) ={circumflex over (x)} _(k) ⁻ +K _(k)(z _(k) −H{circumflex over(x)} _(k) ⁻)P _(k)=(I−K _(k) H)P _(k) ⁻

The first task during the measurement update is to compute the Kalmangain, K_(k), The next step is to actually measure the process to obtain,and then to generate an a posteriori state estimate by incorporating themeasurement, z_(k). The final step is to obtain an a posteriori errorcovariance estimate, P_(k). After each time and measurement update pair,the process is repeated with the previous a posteriori estimates used toproject or predict the new a priori estimates. This recursive nature isone of the very appealing features of the Kalman filter—it makespractical implementations much more feasible than (for example) animplementation of a Wiener filter which is designed to operate on all ofthe data directly for each estimate. The Kalman filter insteadrecursively conditions the current estimate on all of the pastmeasurements. The equation,{circumflex over (x)} _(k) ={circumflex over (x)} _(k) ⁻ +K _(k)(z _(k)−H{circumflex over (x)} _(k) ⁻)is termed the predictor equation.

One of the primary limitations of the Kalman filter is that it onlymodels a linear system with Gaussian distribution, not often encounteredin the physiological setting. The best known algorithm to solve theproblem of non-Gaussian, nonlinear filtering is the extended Kalmanfilter (EKF). This filter is based upon the principle of linearizing themeasurements and evolution models using Taylor series expansions. Theseries approximations in the EKF algorithm can, however, lead to poorrepresentations of the nonlinear functions and probability distributionsof interest. As a result, this filter can diverge. Based on thehypothesis that it is easier to approximate a Gaussian distribution thanit is to approximate arbitrary nonlinear functions other researchershave developed a filter termed the unscented Kalman filter (UKF). It hasbeen shown that the UKF leads to more accurate results than the EKF andthat in particular it generates much better estimates of the covarianceof the states (the EKF often seems to underestimate this quantity). TheUKF has, however, the limitation that it does not apply to generalnon-Gaussian distributions as is often the case with the ECG spectraldistributions. Sequential Monte Carlo methods, also known as particlefilters overcome this limitation and allow for a complete representationof the posterior distribution of the states, so that any statisticalestimates, such as the mean, modes, kurtosis and variance, can be easilycomputed. Particle Filters can therefore, deal with any nonlinearitiesor distributions. Particle filters rely on importance sampling and, as aresult, require the design of proposal distributions that canapproximate the posterior distribution reasonably well. In general, itis hard to design such proposals. The most common strategy is to samplefrom the probabilistic model of the states evolution (transition prior).This strategy can, however, fail if the new measurements appear in thetail of the prior or if the likelihood is too peaked in comparison tothe prior.

In the preferred implementation, a estimator/predictor trajectorytracking technique known as the unscented Particle Filter (UPF) asdeveloped by Merwe, Doucet, Freitasz and Wan. Pseudocode for the UPF isas follows:

Unscented Particle Filter:

Initialization: t=0.

-   -   For i=1, . . . N, draw states (particles) x₀ (from the prior        p(x₀) and set,

${\overset{\_}{x}}_{0}^{(i)} = {E\left\lbrack x_{o}^{(i)} \right\rbrack}$$P_{0}^{(i)} = {E\left\lbrack {\left( {x_{o}^{(i)} - x_{0}^{(i)}} \right)\left( {x_{0}^{(i)} - {\overset{\_}{x}}_{0}^{(i)}} \right)^{T}} \right\rbrack}$${\overset{\_}{x}}_{0}^{{(i)}a} = {{E\left\lbrack x^{{(i)}n} \right\rbrack} = \left\lbrack {\left( {\overset{\_}{x}}_{0}^{(i)} \right)^{T}0\mspace{20mu} 0} \right\rbrack^{T}}$$P_{0}^{{(i)}a} = {{E\left\lbrack {\left( {x_{0}^{{(i)}n} - {\overset{\_}{x}}_{0}^{{(i)}a}} \right)\left( {x_{0}^{{(i)}a} - {\overset{\_}{x}}_{0}^{{(i)}a}} \right)^{T}} \right\rbrack} = \begin{bmatrix}P_{0}^{(i)} & 0 & 0 \\0 & Q & 0 \\0 & 0 & R\end{bmatrix}}$

-   -   For t=1, 2, . . . ,        -   a) Importance sampling step:            -   For i=1, . . . N: Update particles with the UKF:            -   Calculate sigma points:                χ_(i-1) ^((i)u) =[x _(i-1) ^((i)n) x _(i-1)                ^((i)n)±√{square root over ((n _(a)+λ)P _(i-1)                ^((i)n))}]            -   Predict future particle (time update)

χ_(il − 1)^((i)x) = f(χ_(i − 1)^((i)x), χ_(i − 1)^((i)n))$\chi_{i{{l - 1}}}^{{(i)}x} = {\sum\limits_{j = n}^{2n_{\alpha}}{W_{j}^{(m)}\chi_{j,{t{{t - 1}}}}^{{(i)}w}}}$$P_{i{{l - 1}}}^{(i)} = {\sum\limits_{j = 0}^{2n_{a}}{{W_{j}^{(e)}\left\lbrack {\chi_{j,{t{{t - 1}}}}^{{(i)}x} - {\overset{\_}{x}}_{i{{t - 1}}}^{(i)}} \right\rbrack}\left\lbrack {\chi_{j,{t{{t - 1}}}}^{{(i)}x} - {\overset{\_}{x}}_{t{{t - 1}}}^{(i)}} \right\rbrack}^{T}}$y_(il − 1)^((i)) = h(χ_(tt − 1)^((i)x), χ_(t − 1)^((i)u))${\overset{\_}{y}}_{i{{l - 1}}}^{(i)} = {\sum\limits_{j = 0}^{2n_{a}}{W_{j}^{(m)}y_{j,{t{{t - 1}}}}^{(i)}}}$

-   -   -   -   Incorporate new observation (measurement update)

$P_{{\overset{\_}{y}}_{t}{\overset{\_}{y}}_{t}} = {\sum\limits_{j = 0}^{2n_{a}}{{W_{j}^{(e)}\left\lbrack {y_{j,{t{{t - 1}}}}^{(i)} - {\overset{\_}{y}}_{t{{t - 1}}}^{(i)}} \right\rbrack}\left\lbrack {y_{j,{t{{t - 1}}}}^{(i)} - {\overset{\_}{y}}_{t{{t - 1}}}^{(i)}} \right\rbrack}^{T}}$$P_{x_{t}y_{t}} = {\sum\limits_{j = 0}^{2n_{a}}{{W_{j}^{(e)}\left\lbrack {\chi_{j,{t{{t - 1}}}}^{(i)} - {\overset{\_}{x}}_{t{{t - 1}}}^{(i)}} \right\rbrack}\left\lbrack {y_{j,{t{{t - 1}}}}^{(i)} - {\overset{\_}{y}}_{t{{t - 1}}}^{(i)}} \right\rbrack}^{T}}$$K_{t} = {P_{x_{t}y_{t}}P_{{\overset{\_}{y}}_{t}{\overset{\_}{y}}_{t}}^{- 1}}$${\overset{\_}{x}}_{i}^{(i)} = {{\overset{\_}{x}}_{t{{t - 1}}}^{(i)} + {K_{t}\left( {y_{t} - {\overset{\_}{y}}_{i{{t - 1}}}^{(i)}} \right)}}$${\hat{P}}_{t}^{(i)} = {P_{t{{t - 1}}}^{(i)} - {K_{i}P_{{\overset{\_}{y}}_{t}{\overset{\_}{y}}_{t}K_{i}^{T}}} - {{Sample}\mspace{14mu}{\left. {\hat{x}}_{i}^{(i)} \right.\sim{q\left( {{x_{i}^{(i)}\left. {x_{\bullet:{f - 1}}^{(i)},,y_{1:t}} \right)} = {{{{??}\left( {{\overset{\_}{x}}_{i}^{(i)};{\hat{P}}_{i}^{(i)}} \right)} - {{Set}\mspace{14mu}{\hat{x}}_{o:t}^{(i)}}}\overset{\Delta}{=}{\left( {x_{o:{t - 1}}^{(i)},{\hat{x}}_{t}^{(i)}} \right)\mspace{20mu}{and}\mspace{14mu}{{\hat{P}}_{\bullet:i}^{(i)}\left( {P_{0:{t - 1}}^{(i)},{\hat{P}}_{t}^{(i)}} \right)}}}} \right.}}}}$

-   -   -   -   For i=1, . . . N, evaluate the importance weights up to                a normalizing constant:

$w_{t}^{(i)} \propto \frac{p\left( {y_{t}\left. {\hat{x}}_{i}^{(i)} \right){p\left( {{\hat{x}}_{t}^{(i)}\left. x_{t - 1}^{(i)} \right)} \right.}} \right.}{q\left( {{\hat{x}}_{t}^{(i)}\left. {x_{o:{t - 1}}^{(i)},y_{1:t}} \right)} \right.}$

-   -   -   -   For i=1, . . . N, normalize the importance weights.

        -   b) Selection Step            -   Multiply/Suppress particles,                ({circumflex over (x)} _(0:t) ^((i)) ,{circumflex over                (P)} _(0:f) ^((i)))            -   with high/low importance weights,                {tilde over (w)} _(t) ^((i))            -   respectively, to obtain N random particles.

        -   c) Output: The output of the algorithm is a set of samples            that can be used to approximate the posterior distribution            as follows:

$p\left( {{x_{0:t}\left. y_{1:f} \right)} \approx {p\left( {{x_{\bullet:i}\left. y_{1:t} \right)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{\delta_{(x_{0:1}^{(i)})}\left( {\mathbb{d}\; x_{0:i}} \right)}}}} \right.}} \right.$

-   -   -   -   Resulting in the estimate of,

${E\left( {g_{t}\left( x_{0:t} \right)} \right)} = {\int{{g_{f}\left( x_{0:t} \right)}{p\left( {{x_{\bullet:f}\left. y_{1:t} \right){\mathbb{d}\; x_{\bullet:f}}} \approx {\frac{1}{N}{\sum\limits_{i = 1}^{N}{g_{f}\left( x_{\bullet:f}^{(i)} \right)}}}} \right.}}}$for some function of interest, g_(t), for instance the marginalconditional mean or the marginal conditional covariance or other moment.

In one implementation the prediction matrix may be used to anticipatethe optimal therapeutic intervention. Rather than wait for thecharacteristics of the parameters or trajectory to achieve a certaincondition, the algorithm will base its output on the predicted futurestate of the patient using the tracking and prediction algorithmsmentioned above.

Transform methods other than the Fourier method may be employed, forinstance the Laplace, Hilbert, Radon, and Hankel transforms, as well astime frequency transforms such as the Gabor short time Fourier transformand the Wavelet transform.

Other data besides ECG data may be included as part of the descriptionmatrix and incorporated into the analysis algorithm, for instance pulseoximetry, capnography, respiration, impedance cardiography and bloodpressure measurements. At least some of the data may remain in the timedomain without any Fourier or other transform method being performed onit. Pulse oximetry, impedance cardiography, and blood pressuremeasurements may be used to augment the ECG to determine if a pulse ispresent. Capnography may be used to determine the overall effectivenessof cardiopulmonary resuscitation.

Large (˜5″ in diameter), self-adhesive electrode pads are typically usedto deliver defibrillation therapy to patients. The pads also provide ECGmonitoring through the same conductive surfaces. In one implementation,additional small (˜0.5″ diameter) ECG electrodes are integrated into thelarge pads that provide simultaneous monitoring of at least oneadditional electrical vector that is approximately orthogonal to themonitoring vector produced by the large defib/monitoring electrodes. Asecond matrix is then formed, identical in structure to the originalSSM, but based on the orthogonal leads. The AED 10 can then performtechniques such as cross correlation on the two matrices to verify statechanges.

In one embodiment, the two small ECG electrodes and large pads areconfigured such that there at least two mutually orthogonal ECG leadsare generated. The vector sum of these leads generates a trajectory overtime. The same methods for trajectory analysis described above may beused to analyze this trajectory as well.

As described previously, the AED 10 combines these sub-methods todetermine appropriate therapy for the rescuer to perform on the victim.If uncertainty is included in the combination, the probability ofdefibrillation success is shown on the display of the device as a numberbetween zero and one hundred, allowing the trained medical person suchas a paramedic to make his own decision as to whether to shock thepatient. In an implementation where the variance sub-method is used, theAED 10 may be configured such that the VF detection algorithm employingspectral variance may provide notification in the form of an audible orvisual alarm indication that the paramedic should stop doingcompressions for a more accurate analysis of the ECG waveform. In a moreautomated implementation, if the AED 10 determines that defibrillationhas a low probability of success, the AED 10 may prompt the rescuer toperform CPR. During the course of CPR, the AED 10 may analyze the ECGcontinuously and prompt the rescuer to cease doing CPR when the AED 10determines that the myocardium will be receptive to defibrillation.Following the defibrillation, the AED 10 may prompt the rescuer todeliver uninterrupted chest compressions, and the AED 10 may againmonitor the underlying ECG waveform during compressions for theappropriate time to deliver the defibrillation therapy. As a result ofthe spectral analysis, the AED 10 may also determine that neitherdefibrillation nor CPR is appropriate, but rather drug and metabolictherapy such as epinephrine and glucose is appropriate, in which casethe AED 10 will prompt the rescuer to deliver the appropriate therapy.

In another embodiment for determining the appropriate treatment for avictim, the frequency domain of the ECG signal is divided into spectralbands. For example, the frequency range of 3-20 Hz may be divided into0.1 Hz bands. The energy for each band is calculated, and individualweights are assigned to the energy values for each of the bands. In oneembodiment, a summation of at least some of the weighted energy valuesfor each band is calculated.

Regression analysis may be used to determine weights that produceimproved correlation between the weighted sum and the probability ofsuccessful defibrillation (or between the weighted sum and the presenceof a physiological condition). The model for simple linear regressionis:Y=a+b*Xwhere Y is the dependent variable, Xis the independent variable, and aand b are the regression parameters (the intercept and the slope of theline of best fit). The model for multiple linear regression is:Y=a+b ₁ *X ₁ +b ₂ *X ₂ ++b _(i) *X _(i)

The coefficients, b_(i), for each energy, X_(i), are calculated usingstatistical methods such as the general linear model to provide a bestestimate of the probability of defibrillation success, Y. The variable,Y, may also represent the probability of success of any therapeuticintervention other than defibrillation, for instance chest compressions,ventilations or a metabolic treatment such as epinephrine or aspartate.The variable, Y, may also represent the probability that the patient isin a particular physiological state. The general linear model (GLM) canestimate and test any univariate or multivariate general linear model,including those for multiple regression, analysis of variance orcovariance, and other procedures such as discriminant analysis andprincipal components. With the general linear model, randomized blockdesigns, incomplete block designs, fractional factorial designs, Latinsquare designs, split plot designs, crossover designs, nesting, can beexplored. The model is:Y=XB+ewhere Y is a vector or matrix of dependent variables, X is a vector ormatrix of independent variables, B is a vector or matrix of regressioncoefficients, and e is a vector or matrix of random errors.

In multivariate models, Y is a matrix of continuous measures. The Xmatrix can be either continuous or categorical dummy variables,according to the type of model. For discriminant analysis, X is a matrixof dummy variables, as in analysis of variance. For principal componentsanalysis, X is a constant (e.g., a single column of 1 s). For canonicalcorrelation, X is usually a matrix of continuous right-hand variables(and Y is the matrix of left-hand variables).

For some multivariate models, it may be easier to use ANOVA, which canhandle models with multiple dependent variables and zero, one, or morecategorical independent variables (that is, only the constant is presentin the former). ANOVA automatically generates interaction terms for thedesign factor.

After the parameters of a model have been estimated, they can be testedby any general linear hypothesis of the following form:ABC′=Dwhere A is a matrix of linear weights on coefficients across theindependent variables (the rows of B), C is a matrix of linear weightson the coefficients across dependent variables (the columns of B), B isthe matrix of regression coefficients or effects, and D is a nullhypothesis matrix (usually a null matrix).

The coefficients, b_(i), are calculated using ECG or other measuredphysiological data collected from a statistically varied population ofsamples to provide a robust database for accurate model generation.Preferably, the resuscitation event is decomposed into multiple therapystates, e.g., arrival at patient's side, pre-shock, post-shock,post-vasopressor, etc., with separate sets of coefficients generated foreach therapy state. The state of therapy, e.g., resuscitation, isdetermined and stored by the defibrillator. For instance when the unitis first turned on and prior to the first shock, the resuscitation isconsidered in the “arrival at patient's side” (APS) state; if CPR isdetected by the defibrillator, it shifts to the “CPR first, no shockstate”; after defibrillation, the state machine shifts to the “firstshock” state. Subsequent shocks cause the state machine to transition tostates for each defibrillation, e.g. “second shock”, etc. Coefficients,b_(i), are calculated for each state and stored on the defibrillator,and used to calculate the most accurate predictor, Y, of therapeuticoutcome (or current physiologic state). Therapeutic outcome, Y, may bescaled so as to provide a value from either zero to one or zero toone-hundred, representing on a scale that is understandable to theoperator that it is a probability; the value of Y may also be unscaled.

Regression may also be performed using the logistic function:

$Y = {100\left\lbrack {1 - \frac{1}{1 + {\mathbb{e}}^{b_{0} + {\sum{b_{i}x_{i}}}}}} \right\rbrack}$

The logistic model is useful in estimating the probability oftherapeutic success where the outcome is binomial and dependent on atleast one predictive factor, plotted on the abscissa of FIG. 8, suchthat certain values of the predictive factor, e.g. 16 in FIG. 8, willsometimes be associated with successful defibrillation and other timeswith unsuccessful defibrillations. The logistic curve is a non-lineartransformation that converts the measured predictive factor into a valueapproximating a probability of success. It provides a reasonable,mathematically tractable approach to minimizing the false negatives andfalse positives, as shown in FIG. 8. A threshold is chosen thattypically will optimize both the false negatives (FN) and falsepositives (FP) to provide the best sensitivity and specificity for theprediction:Sensitivity=True Positives (TP)/(TP+FN)Specificity=TN/(TN+FP)Positive Predictive Value (PPV)=TP/(TP+FP)Negative Predictive Value (NPV)=TN/(TN+FN)

However, depending on the therapy stage, it may be desirable to optimizefor reduction in false positives at the expense of additional falsenegatives. For instance, when medical personnel first arrive at the sideof a patient, it has been shown in several studies that it is beneficialto many patients that some period of time, typically on the order of 2-3minutes, is spent performing cardiopulmonary resuscitation such as chestcompressions and artificial breathing prior to defibrillation. This hasbeen coined “CPR-first”, and runs counter to how resuscitation ofcardiac arrest has been taught for over a decade. One difficulty withthe method is that for cardiac arrest victims for whom the onset is morerecent, typically on the order of 4 minutes or less, the clinical datasuggests that defibrillation first is a more efficacious therapy forthat class of patient. In this case, a “true negative” is an instancewhen the predictive factor (or measured parameter) is below thethreshold and the outcome was an unsuccessful defibrillation. Becausedefibrillation is necessary to convert ventricular fibrillation, butshocking unnecessarily while not delivering effective CPR is deleteriousand decreases the chances of survival, it is important to minimize asmuch as possible the number of patients in the false negative groupsince these are patients who would have done better had they beenshocked first rather than having CPR-first performed on them. Choosing athreshold in the range of 13-15 will result in a Negative PredictiveValue (NPV) of nearly 100%. Thus the rescuer would only do CPR-first onthose patients for whom there was a very high likelihood that CPR willdo better than defibrillation.

On the other hand, at a later therapy stage (later on in theresuscitation), after an unsuccessful defibrillation shock, doingcontinuous, uninterrupted CPR becomes critical to the survival of thepatient. Thus, it is undesirable to have the rescuer stopping to performpotentially unsuccessful defibrillations. In this resuscitation state,minimizing false positives becomes of primary importance. Raising thethreshold to approximately 20 will result in a Positive predictive Value(PPV) of nearly 100%.

Other therapy stages for which specific thresholds can be set can bebased on the ECG rhythm state of the patient, such as asystole,ventricular fibrillation, ventricular tachycardia, or pulselesselectrical activity.

Therapy stage may also be determined by providing the device with ameans of detecting whether or not the rescuer is performing chestcompressions or ventilations, e.g., by monitoring an accelerometer-basedsensor mounted on the patient's sternum or by measuring thetransthoracic impedance of the patient, such as is done by the AED Prodefibrillator manufactured by ZOLL Medical (Chelmsford Mass.).

Therapy stage may also be determined from data that the rescuer entersinto the device. The device may also have a means for the rescuer toenter treatment data into the device in real time; such data mightinclude whether or not any of the following treatments had been given tothe patient (though not limited to): epinephrine or other vasopressor,levosimendan, aspartate, glucose, intubation, external chest compressordevice, glucose. Treatment data input may be by keying means such as onthe ZOLL Medical (Chelmsford, Mass.) M-Series or E-Seriesdefibrillators. Treatment modes such as pacing and defibrillation can bedistinguished if there is a rotary machine operation dial or knob to setthe unit to mutually exclusive operational modes such as pacing,monitoring, or defibrillation. Other modes might include fluid infusionor ventilation.

A state transition matrix can be developed using a Markov model and thethreshold adjusted as well as different weighting coefficients appliedbased on the Markov model estimation. In particular, the sequence ofmedical interventions and patient reactions to treatments is modeled asa hidden Markov model (HMM), defined as a variant of a finite statemachine having a set of states, Q, an output alphabet, O, transitionprobabilities, A, output probabilities, B, and initial stateprobabilities, II. The current state is not observable. Instead, eachstate produces an output with a certain probability (B). Usually thestates, Q, and outputs, O, are understood, so an HMM is said to be atriple, λ=(A, B, Π). Each value of output alphabet, O, can be given aunique threshold and coefficient set.

-   -   A={a_(ij)=P(q_(j) at t+1|q_(i) at t)}, where P(a|b) is the        conditional probability of a given b, t≧1 is time, and q_(i)εQ.    -   Informally, A is the probability that the next state is q_(j)        given that the current state is q_(i).    -   B={b_(ik)=P(o_(k)|q_(i))}, where o_(k)εO.    -   Informally, B is the probability that the output is o_(k) given        that the current state is q_(i).    -   Π={p_(i)=P(q_(i) at t=1)}.        The Forward-Backward and Baum-Welch algorithms are performed on        a database to build the HMM. A global HMM is developed for all        medical modes along with specific HMMs for each mode such as        pacing, defibrillation, etc.

The Forward-Backward algorithm may be summarized as follows:

Define the α values as follows, α_t(i) = Pr(O_1=o_1,...,O_t=o_t, X_t =q_i | λ) Note that α_T(i) = Pr(O_1=o_1,...,O_T=o_T, X_T = q_i | λ)  =Pr(σ, X_T = q_i | λ) The alpha values enable us to solve Problem 1since, marginalizing, we obtain Pr(σ|λ) = sum_i=1{circumflex over ( )}NPr(o_1,...,o_T, X_T = q_i | λ) = sum_i=1{circumflex over ( )}N α_T(i)Define the β values as follows, β_t(i) = Pr(O_t+1=o_t+1,...,O_T=o_T |X_t = q_i, λ) 1. Compute the forward (α) values: a. α_1(i) = pi_ib_i(o_1) b. α_t+1(j) = [sum_i=1{circumflex over ( )}N α_t(i) a_ij]b_j(o_t+1) 2. Computing the backward (β) values: a. β_T(i) = 1 b. β_t(i)= sum_j=1{circumflex over ( )}N a_ij b_j(o_t+1) β_t+1(j)The Baum-Welch algorithm may be summarized as follows:

The probability of a trajectory being in state q_i at time t and makingthe transition to q_j at t+1 given the observation sequence and model.xi_t(i,j)=Pr(X_t=q_i,X_t+1=q_j|σ,λ)

These probabilities may be computed using the forward backwardvariables.

${{xi\_ t}\left( {i,j} \right)} = \frac{{{\alpha\_ t}(i){a\_ ij}\left( {{o\_ t} + 1} \right){\beta\_ t}} + {1(j)}}{\Pr\left( {O\left. \lambda \right)} \right.}$

The probability of being in q_i at t given the observation sequence andmodel.gamma_t(i)=Pr(X_t=q_i|σ,λ)

Which we obtain by marginalization.γ_t(i)=sum_jxi_t(i,j)

Note thatsum_t=1^Tγ_t(i)=expected number of transitions from q_iandsum_t=1^Txi_t(i,j)=expected number of transitions from q_i to q_j

The algorithm is as follows:

1. Choose the initial parameters, λ, arbitrarily.

2. Reestimate the parameters.

a.  bar{π}_i = γ_t(i)${{b.\mspace{14mu}{bar}}\;\left\{ a \right\}{\_ ij}} = \frac{{sum\_ t} = {{1\hat{}T} - {1\mspace{14mu}{xi\_ t}\left( {i,j} \right)}}}{{sum\_ t} = {{1\hat{}T} - {1\mspace{11mu}{\gamma\_ t}(i)}}}$${{c.\mspace{14mu}{bar}}\left\{ b \right\}{\_ j}(k)} = \frac{{sum\_ t} = {{1\hat{}T} - {1\mspace{14mu}{\gamma\_ t}(j)\mspace{14mu} 1\_\left\{ {{o\_ t} = k} \right\}}}}{{sum\_ t} = {{1\hat{}T} - {1\mspace{14mu}{\gamma\_ t}(j)}}}$

-   -   where 1_{o_t=k}=1 if o_t=k and 0 otherwise.

3. Let bar{A}={bar{a}_ij}, bar{B}={bar{b}_i(k)}, and bar{π}={{bar{π}_i}.

4. Set bar{λ} to be {bar{A}, bar{B}, bar{π}}.

5. If λ=bar{X} then quit, else set λ to be bar{λ} and return to Step 2.

Based on the state transition probabilities calculated by the Baum-Welchalgorithm, the Viterbi algorithm may be used to provide a best estimateof the future sequence of medical interventions that the user willinput.

The Viterbi algorithm may be summarized as follows:

1. Initialization: For 1 <= i <= N, a. δ_1(i) = πb_i(o_1) b. φ_1(i) = 02. Recursion: For 2 <= t <= T, 1 <= j <= N, a. δ_t(j) = max_i[δ_t−1(i)a_ij]b_j(o_t) b. φ_t(j) = argmax_i [δ_t−1(i)a_ij] 3.Termination: a. p* = max_i [δ_T(i)] b. i*_T = argmax_i [δ_T(i)] 4.Reconstruction: For t = t−1,t−2,...,1, i*_t = φ_t+1(i*_t+1) Theresulting trajectory, i*_1,..., i*_t+1, predicts the next likelyintervention, based on the previous sequence.

Many other implementations of the invention other than those describedabove are within the invention, which is defined by the followingclaims.

What is claimed is:
 1. A method of automatically determining which of aplurality of possible types of cardiac interventions should be performedin treatment of a patient, the method comprising: storing first priorinformation representative of types of prior cardiac interventionsperformed on the patient; storing second prior informationrepresentative of the patient's reactions to the prior cardiacinterventions; wherein the first and second prior information are otherthan heart rhythms or ECG waveforms; and processing the first and secondprior information using a hidden Markov model to determine which of aplurality of possible further cardiac interventions should be performed.2. The method of claim 1 further comprising; sensing the patient'sreaction to the further cardiac intervention, storing first furtherinformation representative of the type of the further cardiacintervention; storing second further information representative of thepatient's reaction to the further cardiac intervention, processing thefirst and second prior and first and second further information using ahidden Markov model to determine which of still further cardiacinterventions should be performed in further treatment of the patient.3. The method of claim 1 wherein the patient is a cardiac arrest victim.4. The method of claim 2 wherein the patient is a cardiac arrest victim.5. Apparatus for automatically determining which of a plurality ofpossible types of cardiac interventions should be performed in treatmentof a patient, the apparatus comprising: a processor and associatedmemory for storing first prior information representative of types ofprior cardiac interventions performed on the patient; memory for storingsecond prior information representative of the patient's reactions tothe prior cardiac interventions; wherein the first and second priorinformation are other than heart rhythms or ECG waveforms; and whereinthe processor is configured to process the first and second priorinformation using a hidden Markov model to determine which of aplurality of possible further cardiac interventions should be performed.6. The apparatus of claim 5 further comprising; components for sensingthe patient's reaction to the further cardiac intervention, memory forstoring first further information representative of the type of thefurther cardiac intervention; memory for storing second furtherinformation representative of the patient's reaction to the furthercardiac intervention, and wherein the processor is further configured toprocess the first and second prior and first and second furtherinformation using a hidden Markov model to determine which of stillfurther cardiac interventions should be performed in further treatmentof the patient.
 7. The apparatus of claim 5 wherein the patient is acardiac arrest victim.
 8. The apparatus of claim 6 wherein the patientis a cardiac arrest victim.